Sensor data calibration based on weighted values

ABSTRACT

The subject matter disclosed herein relates to systems, methods and/or devices for calibrating sensor data to be used in estimating a blood glucose concentration. A relationship between sensor measurements and reference readings may be used to estimate a relationship between sensor measurements and blood glucose concentration. Such sensor measurements may be weighted according to a decreasing function of uncertainty associated with sensor values.

BACKGROUND Field

The subject matter disclosed herein relates to calibration of glucosesensors for use in glucose monitoring systems, for example.

Information

Over the years, body characteristics have been determined by obtaining asample of bodily fluid. For example, diabetics often test for bloodglucose levels. Traditional blood glucose determinations have utilized apainful finger prick using a lancet to withdraw a small blood sample.This results in discomfort from the lancet as it contacts nerves in thesubcutaneous tissue. The pain of lancing and the cumulative discomfortfrom multiple needle pricks is a strong reason why patients fail tocomply with a medical testing regimen used to determine a change in abody characteristic over a period of time. Although non-invasive systemshave been proposed, or are in development, none to date have beencommercialized that are effective and provide accurate results. Inaddition, all of these systems are designed to provide data at discretepoints and do not provide continuous data to show the variations in thecharacteristic between testing times.

A variety of implantable electrochemical sensors have been developed fordetecting and/or quantifying specific agents or compositions in apatient's blood. For instance, glucose sensors are being developed foruse in obtaining an indication of blood glucose levels in a diabeticpatient. Such readings are useful in monitoring and/or adjusting atreatment regimen which typically includes the regular administration ofinsulin to the patient. Thus, blood glucose readings improve medicaltherapies with semi-automated medication infusion pumps of the externaltype, as generally described in U.S. Pat. Nos. 4,562,751; 4,678,408; and4,685,903; or automated implantable medication infusion pumps, asgenerally described in U.S. Pat. No. 4,573,994. Typical thin filmsensors are described in commonly assigned U.S. Pat. Nos. 5,390,671;5,391,250; 5,482,473; and 5,586,553. See also U.S. Pat. No. 5,299,571.

SUMMARY

Briefly, one embodiment relates to a method, system and/or apparatus forobtaining samples of an electrical signal generated by a sensor, saidsamples having sample values associated with measurements of ablood-glucose concentration; individually weighting at least some ofsaid sample values according to a function of blood glucose referencesamples associated with said sample values; and estimating arelationship of sample values with said blood-glucose concentrationbased, at least in part, on said individually weighted samples.

In another implementation estimating said relationship comprisesestimating a linear relationship between said sample values and saidblood-glucose concentration based, at least in part, on a linearregression of said weighted samples and associated blood-glucosereference values. Here, for example, such estimating said linearrelationship may further comprise calculating a linear regressionsensitivity ratio based, at least in part, on said weighted samples andassociated blood-glucose reference values; selecting an offset based, atleast in part, on said calculated linear regression sensitivity ratio;and calculating a modified linear regression sensitivity ratio based, atleast in part, on said selected offset, said weighted samples and saidassociated blood-glucose reference values.

In another particular implementation the function of blood glucosereference samples is based, at least in part, on a measure ofstatistical dispersion of said sample values as function of associatedblood glucose reference samples. Here, for example, said measure ofstatistical dispersion may comprise a variance and/or approximation of avariance of said sample values as a function of said associated bloodglucose reference samples. Alternatively, the function may comprise aninverse of said measure of statistical dispersion of said sample values.In yet another alternative, the method includes estimating a linearrelationship of said measure of statistical dispersion of said samplevalues versus blood glucose concentration; and deriving the functionbased, at least in part, on said linear relationship.

In another particular implementation, individually weighting said atleast some of said sample values further comprises further weightingsaid samples based on how recently said samples are obtained.

In another particular implementation, the method includes detecting afailure of said sensor based, at least in part, on a change in saidestimated relationship.

In another particular implementation, the method includes calibratingmeasurements from said sensor for measuring a blood-glucoseconcentration based, at least in part, on said estimated relationship.

In another particular implementation, individually weighting said atleast some of said sample values comprises weighting said at least someof said sample values according to a decreasing function of bloodglucose reference values associated with said weighted samples.

Particular embodiments may be directed to an article comprising astorage medium including machine-readable instructions stored thereonwhich, if executed by a computing platform, are directed to enable thecomputing platform to execute at least a portion of the aforementionedmethod according to one or more of the particular aforementionedimplementations. In other particular embodiments, a sensor adaptedgenerate one or more signals responsive to a blood glucose concentrationin a body while a computing platform is adapted to perform theaforementioned method according to one or more of the particularaforementioned implementations based upon the one or more signalsgenerated by the sensor.

BRIEF DESCRIPTION OF THE FIGURES

Non-limiting and non-exhaustive features will be described withreference to the following figures, wherein like reference numeralsrefer to like parts throughout the various figures.

FIG. 1 is a is a perspective view illustrating a subcutaneous glucosesensor insertion set and glucose monitor device in accordance with anembodiment;

FIG. 2 is a cross-sectional view of the sensor set and glucose monitordevice as shown along the line 2-2 of FIG. 1 ;

FIG. 3 is a cross-sectional view of a slotted insertion needle used inthe insertion set of FIGS. 1 and 2 ;

FIG. 4 is a cross-sectional view as shown along line 4-4 of FIG. 3 ;

FIG. 5 is a cross-sectional view as shown along line 5-5 of FIG. 3 ;

FIG. 6 is a partial cross-sectional view corresponding generally withthe encircled region 6 FIG. 2 ;

FIG. 7 is a cross-sectional view as shown along line 7-7 of FIG. 2 ;

FIGS. 8 a through 8 c are diagrams showing a relationship betweensampled values, interval values and memory storage values according toan embodiment;

FIG. 9 is a chart showing clipping limits according to an embodiment;

FIG. 10 is a sample computer screen shot image of a post processoranalysis of glucose monitor data according to an embodiment;

FIG. 11 is a chart illustrating the pairing of a blood glucose referencereading with glucose monitor data according to an embodiment;

FIG. 12 is a chart illustrating an example of a single-point calibrationaccording to an embodiment;

FIG. 13 is a block diagram illustrating a single-point calibrationtechnique according to an embodiment;

FIG. 14 is a chart illustrating an example of a linear regressioncalibration according to an embodiment;

FIG. 15 a is a flow diagram illustrating a calibration process accordingto an embodiment;

FIG. 15 b is a plot of sensor measurements versus reference bloodsamples according to an embodiment;

FIG. 15 c is a plot of an inverse variance of sensor measurements versusblood glucose concentration according to an embodiment;

FIG. 15 d is a plot illustrating a linear best fit of a standarddeviation of sensor measurements versus blood glucose concentrationaccording to an embodiment;

FIG. 15 e is a plot of a function for obtaining weights to be applied tosensor sample values according to an embodiment;

FIG. 16 is a flowchart of a self-adjusting calibration technique inaccordance with an embodiment;

FIGS. 17 a and 17 b are charts illustrating an example of theself-adjusting calibration technique according to an embodiment; and

FIGS. 18 a and 18 b are further charts illustrating an example of theself-adjusting calibration technique according to an embodiment.

DETAILED DESCRIPTION

Reference throughout this specification to “one embodiment” or “anembodiment” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment of claimed subject matter. Thus, theappearances of the phrase “in one embodiment” or “an embodiment” invarious places throughout this specification are not necessarily allreferring to the same embodiment. Furthermore, the particular features,structures, or characteristics may be combined in one or moreembodiments.

Systems for monitoring glucose in the body, for the treatment ofdiabetes for example, typically employ one or more glucose sensors tomeasure a blood-glucose concentration. For example, such sensors may beadapted to generate one or more electrical signals having a value (e.g.,voltage and/or current level) that is related to such a blood-glucoseconcentration. Such a measurement of a blood-glucose concentration maythen be used for any one of several applications such as, for example,monitoring a blood-glucose concentration for a diabetes patient.

Over time and/or with normal wear and usage of a glucose sensor, such arelationship between a value of a signal generated by the glucosemonitoring blood sensor and actual measured blood glucose concentrationmay change. Accordingly, calibration of the signal generated by such aglucose monitoring with reference samples of blood-glucose concentrationmay enable an accurate estimate of a relationship between signal valuesgenerated by a glucose sensor and blood-glucose concentration, leadingto more effective applications of glucose sensors and better treatmentof diabetes patients.

As shown in the drawings for purposes of illustration, embodiments aredirected to calibration methods for a glucose monitor that is coupled toa sensor set to provide continuous data recording of readings of glucoselevels from a sensor for a period of time. In one particularimplementation, a sensor and monitor provide a glucose sensor and aglucose monitor for determining glucose levels in the blood and/orbodily fluids of a user. However, it will be recognized that furtherembodiments may be used to determine the levels of other bodycharacteristics including, for example, analytes or agents, compounds orcompositions, such as hormones, cholesterol, medications concentrations,viral loads (e.g., HIV), bacterial levels, or the like without deviatingfrom claimed subject matter. In particular implementations, a glucosesensor is primarily adapted for use in subcutaneous human tissue.However, in still further embodiments, one or more sensors may be placedin other tissue types, such as muscle, lymph, organ tissue, veins,arteries or the like, and used in animal tissue to measure bodycharacteristics. Embodiments may record readings from the sensor on anintermittent, periodic, on-demand, continuous, or analog basis.

According to an embodiment, a blood glucose concentration in fluid maybe measured based upon values of a sampled sensor signal. Also, asdiscussed below, it can be observed in particular embodiments that theaccuracy of such measurements used to measure blood glucoseconcentration may decrease with increases in blood glucoseconcentration. Accordingly, as illustrated below, in estimating a bloodglucose response of a particular sensor, measurements taken at lowerblood glucose concentrations may be more heavily weighted than samplestake a higher blood glucose concentrations.

Briefly, in one particular embodiment, an electrical signal generated bya sensor may be sampled to provide sample values associated with ablood-glucose concentration. Uncertainty values may be associated withindividual ones of the measurements based, at least in part, onblood-glucose reference values associated with the measurements. Atleast some of the sample values are weighted according to a decreasingfunction of uncertainty values associated with the sample values. Arelationship of sample values with blood-glucose concentration may thenbe determined based, at least in part, on the individually, weightedsample values. It should be understood, however, this is merely anexample embodiment and claimed subject matter is not limited in thisrespect.

FIGS. 1-7 illustrate a glucose monitor system 1 for use with calibrationmethods described herein. Glucose monitor system 1, in accordance withone particular implementation, includes a subcutaneous glucose sensorset 10 and a glucose monitor 100. Here, glucose monitor 100 may be ofthe type described in U.S. patent application Ser. No. 60/121,664, filedon Feb. 25, 1999, entitled “Glucose Monitor System.” In alternativeembodiments, the glucose monitor is of the type described in U.S. Pat.No. 7,324,012.

In one particular application, glucose monitor 100 may be worn by a userwhile connected to a surface mounted glucose sensor set 10 attached tothe user's body by an electrically conductive cable 102, of the typedescribed in U.S. Patent Application Ser. No. 60/121,656, filed on Feb.25, 1999, entitled “Test Plug and Cable for a Glucose Monitor.” In oneembodiment, a sensor interface may be configured in the faun of a jackto accept different types of cables that provide adaptability of theglucose monitor 100 to work with different types of subcutaneous glucosesensors and/or glucose sensors placed in different locations of a user'sbody. However, in alternative embodiments, such a sensor interface maybe permanently connected to the cable 102. In additional alternativeembodiments, a characteristic monitor may be connected to one or moresensor sets to record data of one or more body characteristics from oneor more locations on or in a user's body.

According to an embodiment, glucose sensor set 10 may be of a typedescribed in U.S. Patent Application Ser. No. 60/121,655, filed on Feb.25, 1999, entitled “Glucose Sensor Set”, or U.S. patent application Ser.No. 08/871,831, filed on Jun. 9, 1997, entitled “Insertion Set For ATranscutaneous Sensor.” Glucose sensor 12 may be of a type described inU.S. patent application Ser. No. 09/101,218, filed on Feb. 25, 1999,entitled “Glucose Sensor”, or described in commonly assigned U.S. Pat.Nos. 5,390,671; 5,391,250; 5,482,473; and 5,586,553; extends from theglucose sensor set 10 into a user's body with electrodes 20 of theglucose sensor 12 terminating in the user's subcutaneous tissue. Seealso U.S. Pat. No. 5,299,571. However, in alternative embodiments,glucose sensor 12 may use other types of sensors, such as chemicalbased, optical based, or the like. In further alternative embodiments,sensors may be of a type that is used on the external surface of theskin or placed below the skin layer of the user for detecting bodycharacteristics.

According to an embodiment, glucose monitor 100 may be capable ofrecording and storing data as it is received from glucose sensor 12, andmay include either a data port (not shown) or wireless transmitterand/or receiver (also not shown) for transferring data to and/or from adata processor 200 such as a computer, communication station, adedicated processor designed specifically to work with the glucosemonitor, or the like. In a particular implementation, glucose monitor100 may comprise a glucose monitor as described in U.S. Pat. No.7,324,012.

In particular applications, glucose monitor system 1 may reduceinconvenience by separating complicated monitoring process electronicsinto two separate devices; the glucose monitor 100, which attaches tothe glucose sensor set 10; and the data processor 200, which containsthe software and programming instructions to download and evaluate datarecorded by the glucose monitor 100. In addition, the use of multiplecomponents (e.g., glucose monitor 100 and data processor 200) mayfacilitate upgrades or replacements, since one module, or the other, canbe modified, re-programmed, or replaced without requiring completereplacement of the monitor system 1. Further, the use of multiplecomponents can improve the economics of manufacturing, since somecomponents may require replacement on a more frequent basis, sizingrequirements may be different for each module, different assemblyenvironment requirements, and modifications can be made withoutaffecting the other components.

Glucose monitor 100 may take raw glucose sensor data from glucose sensor12 and assess such sensor data in real-time and/or stores it for laterprocessing or downloading to data processor 200, which in turn mayanalyze, display, and log the received data. Data processor 200 mayutilize the recorded data from the glucose monitor 100 to analyze andreview a blood glucose history. In particular embodiments, glucosemonitor 100 is placed into a com-station which facilitates downloadingdata to a personal computer for presentation to a physician. Softwaremay be used to download such data, create a data file, calibrate thedata, and display such data in various formats including charts, forms,reports, graphs, tables, lists and/or the like. In further embodiments,glucose monitor system 1 may be used in a hospital environment and/orthe like.

In alternative embodiments, glucose monitor 100 may include at leastportions of the software described as contained within the dataprocessor 200 above. Glucose monitor 100 may further contain softwareenabling calibration of glucose sensor signals, display of a real-timeblood glucose value, a showing of blood glucose trends, activate alarmsand the like. A glucose monitor with these added capabilities is usefulfor patients that might benefit from real-time observations of theirblood glucose characteristics even while they're not in close proximityto a computer, communication device and/or dedicated independent dataprocessor.

As shown in FIG. 2 , data processor 200 may include a display 214adapted to display calculated results of raw glucose sensor datareceived via a download from glucose monitor 100. Results andinformation displayed may include, but is not limited to, trendinginformation of a characteristic (e.g., rate of change of glucose),graphs of historical data, average characteristic levels (e.g.,glucose), stabilization and calibration information, raw data, tables(showing raw data correlated with the date, time, sample number,corresponding blood glucose level, alarm messages, and more) and/or thelike. Alternative embodiments may include an ability to scroll throughraw data. Display 214 may also be used in conjunction with buttons (notshown) on the data processor 200, computer, communication station,characteristic monitor and/or or the like, to program or update data.

Glucose monitor 100 may be combined with other medical devices to acceptother patient data through a common data network and/or telemetrysystem. Glucose monitor 100 may be combined with a blood glucose meterto directly import or correlate glucose calibration reference valuessuch as described in U.S. patent application Ser. No. 09/334,996, filedJun. 17, 1999, entitled “Characteristic Monitor With A CharacteristicMeter and Method Of Using The Same.” Glucose monitor 100 may also becombined with semi-automated medication infusion pumps of the externaltype, as described according to particular embodiments in U.S. Pat. Nos.4,562,751; 4,678,408; and 4,685,903; or automated implantable medicationinfusion pumps, as described according to particular embodiments in U.S.Pat. No. 4,573,994. Glucose monitor 100 may record data from theinfusion pumps and/or may process data from both the glucose sensor 12and an infusion pump to establish a closed loop system to control theinfusion pump based, at least in part, on glucose sensor measurements.In other embodiments, other body characteristics are monitored, and themonitor may be used to provide feedback in a closed loop system tocontrol a drug delivery rate. In further alternative embodiments,glucose monitor 100 can be combined with a glucose sensor set 10 as asingle unit.

Glucose sensors may be replaced periodically to avoid infection,decaying enzyme coating and therefore sensor sensitivity, deoxidizationof the electrodes, and/or the like. Here, a user may disconnect glucosesensor set 10 from cable 102 and glucose monitor 100. A needle 14 may beused to install another glucose sensor set 10, and then the needle 14may be removed. Further description of the needle 14 and sensor set 10according to particular embodiments may be found in U.S. Pat. Nos.5,586,553; 6,368,141 and 5,951,521.

A user may connect connection portion 24 of glucose sensor set 10through cable 102 to glucose monitor 100, so that glucose sensor 12 canthen be used over a prolonged period of time. An initial reading may bedownloaded from the glucose sensor set 10 and glucose monitor 100 todata processor 200, to verify proper operation of glucose sensor 10 andglucose monitor 100. In particular embodiments, glucose sensor set 10may provide data to glucose monitor 100 for one to seven days beforereplacement. Glucose sensors 12 may last in the user's body for longeror shorter periods of time depending on the quality of the installation,cleanliness, the durability of the enzyme coating, deoxidization of thesensor, user's comfort, and the like.

After installation into the body, glucose sensor 12 may be initializedto achieve a steady state of operation before starting a calibrationprocess. In a particular implementation, power supplied by three seriessilver oxide 357 battery cells 110 in glucose monitor 100 may be used tospeed the initialization of glucose sensor 12. Alternatively, otherpower supplies may be used such as, different battery chemistriesincluding lithium, alkaline, or the like, and different numbers ofbatteries, solar cells, a DC converter plugged into an AC socket(provided with proper electrical isolation), and/or the like.

The use of an initialization process can reduce the time for glucosesensor 12 stabilization from several hours to an hour or less, forexample. One particular initialization procedure uses a two stepprocess. First, a high voltage (e.g., between 1.0-1.1 volts—althoughother voltages may be used) may be applied between electrodes 20 of thesensor 12 for one to two minutes (although different time periods may beused) to allow sensor 12 to stabilize. Then, a lower voltage (e.g.,between 0.5-0.6 volts—although other voltages may be used) may beapplied for the remainder of the initialization process (e:g., 58minutes or less). Other stabilization/initialization procedures usingdiffering currents, currents and voltages, different numbers of steps,or the like, may be used. Other embodiments may omit such aninitialization/stabilization process, if not required by a particularbody characteristic sensor or if timing is not a factor. Alternatively,a characteristic monitor or data processor 200 may apply an algorithm tothe sensor data to determine whether initial transients havesufficiently diminished and the sensor is at a significantly stablestate to begin calibration.

In particular embodiments, data may not be considered valid until asensor initialization event flag (ESI) is set in data indicating thatstabilization is complete. In one particular implementation,stabilization may be complete after 60 minutes or when a user enters asensor initialization flag using one or more buttons on the glucosemonitor 100. Following completion of stabilization/initialization,glucose monitor 100 may be calibrated to accurately interpret readingsfrom the newly installed glucose sensor 12.

Beginning with the stabilization process, glucose monitor 100 maymeasure a continuous electrical current signal (ISIG) generated byglucose sensor 12 in connection with a concentration of glucose presentin the subcutaneous tissue of the user's body. In particularembodiments, glucose monitor 100 may sample the ISIG from glucose sensor12 at a sampling rate of once every 10 seconds, for example, as shown inFIGS. 8 a -c. Examples of sampled values are labeled A-AD in FIG. 8 a .At an interval rate of once per minute, the highest and lowest of thesampled values (shown in FIG. 8 a as circled sampled values A, E, G, I,M, R, V, W, Y, and AB) are ignored, and the remaining four sampledvalues from an interval are averaged to create interval values (shown inFIG. 8 b as values F′, R′, X′, and AD′). At a glucose monitor memorystorage rate of once every five minutes, the highest and lowest of theinterval values (shown in FIG. 8 b as values L′ and X′) are ignored andthe remaining three interval values are averaged and stored in a glucosemonitor memory as memory values (shown in FIG. 8 c as point AD″). Thememory values are retained in memory and may be downloaded to dataprocessor 200. Such memory values may be used to calibrate glucosemonitor 100 and/or post processor 200 and to analyze blood glucoselevels. The sampling rate, interval rate and the memory storage rate maybe varied as necessary to capture data with sufficient resolution toobserve transients or other changes in the data depending on the rate atwhich sensor values can change, which is affected by the sensorsensitivity, the body characteristic being measured, the physical statusof the user, and the like. In other embodiments, all of the sampledvalues are included in the average calculations of memory storagevalues. In alternative embodiments, more or less sampled values orinterval values are ignored depending on the signal noise, sensorstability, or other causes of undesired transient readings. Finally, instill other embodiments, all sampled values and/or interval values arestored in memory.

Clipping limits may be used to limit a signal magnitude variation fromone value to the next thereby reducing the effects of extraneous data,outlying data points, or transients. In particular embodiments, clippinglimits may be applied to interval values. For instance, interval valuesthat are above a maximum clipping limit or below a minimum clippinglimit may be replaced with the nearest clipping limit value.

In alternative embodiments, interval values that are outside of clippinglimits may be ignored and not used to calculate a subsequent memorystorage value. In particular implementations, detection of intervalvalues outside of clipping limits may be considered a calibrationcancellation event. In further particular embodiments, a calibrationcancellation event may be recognized if more than one value is deemedoutside of clipping limits. (Calibration cancellation events arediscussed below).

In particular embodiments, clipping limits may be shifted after eachdata point. Here, clipping limits may be set to a level based, at leastin part, on an acceptable amount of change from a previous intervalvalue to a present interval value, which is affected by sensorsensitivity, signal noise, signal drift, and/or the like. In particularimplementations, clipping limits may be calculated for a currentinterval based on the magnitude of the previous interval value. Forexample, for a previous interval value from zero up to but not including15 Nano-Amps, clipping limits may be set at plus and minus 0.5 Nano-Ampsabout the previous interval value. For a previous interval value from 15Nano-Amps up to but not including 25 Nano-Amps, clipping limits may beset at plus and minus 3% of the previous interval value, about theprevious interval value. For a previous interval value from 25 Nano-Ampsup to but not including 50 Nano-Amps, clipping limits may be set at plusand minus 2% of the previous interval value, about the previous intervalvalue. For a previous interval value of 50 Nano-Amps and greater,clipping limits may be set at plus and minus 1% about the previousinterval value. In alternative embodiments, different clipping limitsmay be used and claimed subject matter is not limited in this respect.

FIG. 9 shows a clipping limit example according to a particularembodiment in which a previous interval value 500, associated withinterval N-1, has a magnitude of 13.0 Nano-Amps, which is less than 15.0Nano-Amps. Therefore, a maximum clipping limit 502 for a presentinterval value 506 is set at 13.5 Nano-Amps, which is 0.5 Nano-Ampsgreater than the magnitude of the previous interval value 500. A minimumclipping limit 504 is set at 12.5 Nano-Amps which is 0.5 Nano-Amps belowthe previous interval value 500. Present interval value 506, associatedwith interval N, is between the maximum clipping limit 502 and theminimum clipping limit 504 and is therefore acceptable.

In another example shown in FIG. 9 , the present interval value 508,associated with interval M, has a value of 25.0 Nano-Amps which isoutside of the clipping limit 514 and will therefore be clipped. Theprevious interval value 510, associated with interval M-1, is 26.0Nano-Amps, which is included in the range from 25.0 up to but notincluding 50.0 Nano-Amps as discussed above. Therefore the clippinglimits are +/−2%. The maximum clipping limit 512 is 2% greater than theprevious interval value 510 as follows:

26.0+26.0*0.02=26.5 Nano-Amps.

Similarly the minimum clipping limit 514 is 2% less than the previousinterval value 510 as follows:

26.0−26.0*0.02=22.5 Nano-Amps.

Since the present interval value 508 of 25.0 Nano-Amps is less than theminimum clipping limit 514 of 25.5 Nano-Amps, it will be clipped, and25.5 Nano-Amps will be used in place of 25.0 Nano-Amps to calculate amemory storage value. For further illustration, FIG. 8 shows intervalvalue R′, which is calculated by averaging sampled values N through Q,is outside of the clipping limits 412 and 414, which result from theprevious interval value L′. Therefore, in this particular example, themagnitude of interval value R′ is not used to calculate memory valueAD″, instead R″, which is the magnitude of the minimum clipping limit414, is used.

In other embodiments, clipping limits may be a smaller or larger numberof Nano-Amps or a smaller or larger percentage of the previous intervalvalue based on the sensor characteristics mentioned above.Alternatively, clipping limits may be calculated as plus or minus thesame percent change from every previous interval value. Other algorithmsmay use several interval values to extrapolate the next interval valueand set the clipping limits to a percentage higher and lower than thenext anticipated interval value. In further alternatives, clipping maybe applied to the sampled values, interval values, memory values,calculated glucose values, estimated values of a measuredcharacteristic, or any combination of such values.

In particular embodiments, interval values are compared to anout-of-range limit of 200 Nano-Amps. If three consecutive intervalvalues are equal to or exceed the out-of-range limit, the sensorsensitivity may be deemed to be too high, and an alarm is activated tonotify the user that re-calibration is required or the sensor may needreplacing. In alternative embodiments, an out-of-range limit may be setat higher or lower values depending on the range of sensorsensitivities, the expected working life of the sensor, the range ofacceptable measurements, and/or the like. In particular embodiments, anout-of range limit is applied to sampled values. In other embodiments,an out-of-range limit is applied to the memory storage values.

In particular embodiments, unstable signal alarm limits may be set todetect drastic changes in memory storage values from one to another.Signal alarm limits may be established similarly to the clipping limitsdescribed above for the interval values, but allow for a larger changein value since there is more time between memory storage values thanbetween interval values. Re-calibration or replacement of the glucosesensor 12 may be performed once an unstable signal alarm is activated.In essence, in a particular implementation, such an alarm is thereforeactivated in the event that glucose monitor 100 detects an unacceptablelevel of noise in the ISIG from glucose sensor 12.

In a particular embodiment, a memory storage value may be consideredvalid (Valid ISIG value) unless one of the following calibrationcancellation events occurs: an unstable signal alarm (as discussedabove); a sensor initialization event (as discussed above); a sensordisconnect alarm; a power on/off event; an out-of-range alarm (asdiscussed above); or a calibration error alarm. Here, only Valid ISIGvalues may be used to calculate blood glucose levels by the glucosemonitor 100 or post processor 200, as shown in FIG. 10 . Once acalibration cancellation event occurs, successive memory storage valuesare not valid, and therefore are not used to calculate blood glucose,until glucose monitor 100 or post processor 200 is re-calibrated. FIG.10 shows an explanatory computer screen shot in which cell P3 indicatesa sensor disconnect alarm with the abbreviation “SeDi”. As shown, bloodglucose values do not appear in column K, titled “Sensor Value”, andValid ISIG values do not appear in column J until after the sensor isinitialized, as indicated by the “ESI” flag in cell N17. One exceptionhowever, is the power on/off event. If glucose monitor 100 is turned offfor a short enough period of time, up to 30 minutes for example, memorystorage values may be considered Valid ISIG values as soon as the poweris restored. If the power is off for longer than 30 minutes, forexample, glucose monitor 100 may be re-calibrated before ISIG values areconsidered valid. Alternatively, power may be off for a duration such as30 minutes or longer and, once power is restored, the memory storagevalues may comprise Valid ISIG values. Here, a sensor disconnect alarmmay be activated if the glucose monitor 100 does not detect a signal. Inpreferred embodiments, when two or more out of five interval valuescollected within a given memory storage rate are less than 1.0 Nano-Amp,a disconnect alarm may be triggered. In alternative embodiments, greateror fewer values need be below a particular threshold current level totrigger a disconnect alarm depending of the acceptable range or sensorreadings and the stability of an associated sensor signal. Two remainingcalibration cancellation events, the calibration error and analternative embodiment for the out-of-range alarm, are discussed inconjunction with the calibration process below.

Particular implementations are directed to calibration techniques thatmay be used by either glucose monitors during real-time measurements ofone or more signals from a glucose sensor, or post processors duringpost-processing of data that has been previously recorded and downloaded(as shown in FIG. 10 ).

To calibrate glucose monitor 100, a calibration factor called asensitivity ratio (SR) (blood glucose level/Valid ISIG value) may becalculated for a particular glucose sensor 12. The SR is a calibrationfactor used to measure/estimate a blood glucose concentration based, atleast in part on a Valid ISIG value (Nano-Amps) into a blood glucoselevel (mg/dl or mmol/l). In alternative embodiments, units for the SRmay vary depending on the type of signal available from the sensor(frequency, amplitude, phase shift, delta, current, voltage, impedance,capacitance, flux, and the like), the magnitude of the signals, theunits to express the characteristic being monitored, and/or the like.

In particular implementations, a user may obtain a blood glucosereference reading from a common glucose meter, or another blood glucosemeasuring device, and immediately enter such a blood glucose referencereading into glucose monitor 100. Such a blood glucose reference readingis assumed to be accurate and is used as a reference for calibration.Glucose monitor 100, or a post processor 200, may temporally correlate ablood glucose reference reading with a Valid ISIG value to establish a“paired calibration data point.” Since a glucose level in aninterstitial body fluid tends to lag behind a blood glucose level,glucose monitor 100 or post processor 200 applies a delay time and thenpairs the blood glucose reference reading with a Valid ISIG value asshown in FIG. 11 . In particular embodiments, an empirically derived tenminute delay may be used. In a particular implementation where ValidISIG values are averaged and stored every five minutes, glucose monitor100 may correlate a blood glucose reference reading with the third ValidISIG stored in memory after the blood glucose reference reading isentered (resulting in an effective delay of ten to fifteen minutes inthis particular example). FIG. 11 illustrates an example, in which ablood glucose reference reading 600 of 90 mg/dl is entered into theglucose monitor 100 at 127 minutes. The next Valid ISIG value 602 may bestored at 130 minutes. Given a 10 minute delay, a glucose referencereading 600 may be paired with Valid ISIG value 604 which is stored at140 minutes with a value of 30 Nano-amps. Note that two numbers areneeded to establish one paired calibration data point, a blood glucosereference reading and a Valid ISIG.

Other delay times may be used depending on a particular user'smetabolism, response time of the sensor, delay time incurred for theglucose meter to calculate a reading and for the reading to be enteredinto the glucose monitor 100, a type of analyte being measured, thetissue that the sensor is placed into, environmental factors, whetherthe previous glucose Valid ISIG value (or the trend of the Valid ISIGvalues) was higher or lower than current Valid ISIG value, and/or thelike. Once paired calibration data is available, the appropriatecalibration process may be applied dependent on how many pairedcalibration data points are available since the last calibration, thetotal period of time that glucose sensor 12 has been in use, and thenumber of times glucose sensor 12 has been calibrated.

In particular embodiments, blood glucose reference readings may beentered into glucose monitor 100 periodically through out each day ofuse. Here, calibration may be conducted immediately after theinitialization/stabilization of glucose sensor 12 and once a daythereafter. However, such calibration may be conducted more or lessoften depending on whether glucose sensor 12 has been replaced, whethera calibration cancellation event has occurred, the stability of glucosesensor 12 sensitivity over time, and/or the like.

In preferred embodiments, blood glucose reference readings are collectedseveral times per day but a new calibration factor is calculated onlyonce per day. Therefore, typically more than one paired calibration datapoint is collected between calibrations. In alternative embodiments, theglucose monitor is calibrated every time a new paired calibration datapoint is collected.

Particular embodiments may use a single-point calibration technique(shown in a block diagram of FIG. 13 ) to calculate the SR if only asingle paired calibration data point is available, such as immediatelyafter initialization/stabilization. And a modified linear regressiontechnique (shown in a block diagram in FIG. 15 a ) may be used if two ormore paired calibration data points are available. Particularembodiments may use a single-point calibration technique whether or notmore than one paired calibration data point is available.

A single-point calibration equation may be based on an assumption that aValid ISIG will be 0 when the blood glucose is 0. As shown in process750 of FIG. 12 , a single paired calibration point 700 obtained at block754 is used with the point (0,0) to establish a line 702. The slope ofthe line from the origin (0,0) and passing through the single pairedcalibration point 700 provides a single-point sensitivity ratio (SPSR).Here, block 756 may calculate such an SPSR as follows:

${SPSR} = \frac{{Blood}{Glucose}{Reference}{Reading}}{{Valid}{ISIG}}$

Therefore, the calibrated blood glucose level may be expressed asfollows:

Blood Glucose Level=Valid ISIG*SPSR

As an example, using the values of 20.1 Nano-Amps and 102 mg/dl from thepaired calibration data point shown in FIG. 12 , calculation of SPSR maybe expressed as follows:

SPSR=102/20.1=5.07 mg/dl per Nano-Amp

To continue with the current example, once calibration is complete,given a glucose sensor reading of 15.0 Nano-Amps, calculated bloodglucose level may be determined as follows:

Blood Glucose Level=15.0*5.07=76.1 mg/dl

Additionally, particular embodiments may use an offset value in acalibration equation to compensate for the observation that moresensitive glucose sensors 12 (e.g., glucose sensors 12 that generatehigher ISIG values compared to other glucose sensors 12 at the sameblood glucose level, which result in lower SR values) may have a lesslinear performance at very high blood glucose levels in comparison toglucose sensors 12 with lower sensitivity (and therefore relativelyhigher SR values). If the SPSR for a particular glucose sensor 12, ascalculated above, is less than a sensitivity threshold value, then amodified SPSR (MSPSR) may be calculated at block 760 using an offsetvalue selected at block 758. In one particular implementation, thethreshold value is 7. If the initial calculation of the SPSR (shownabove) is less than 7, for example, an offset value of 3 may be used tocalculate the MSPSR. If the initial calculation of SPSR yields a valueof 7 or greater, then the offset value may be 0. Thus, the MSPSR may becalculated at block 760 using the offset value according to a modifiedsingle-point calibration expression, as follows:

${MSPSR} = \frac{{Blood}{Glucose}{Reference}{Reading}}{{{Valid}{ISIG}} - {offset}}$

Accordingly, an initial calibration of sensor 12 may be used to estimatea blood glucose from a sensor measurement at block 762 as follows:

Blood Glucose Level=(Valid ISIG−offset)*SPSR

Continuing the above example since the SPSR is 5.07, which is less than7, the sensitivity ratio is recalculated using the MSPSR equation as:

MSPSR=102/(20.1−3)=5.96 mg/dl per Nano-Amp

Given a glucose sensor reading of 15.0 Nano-Amps after calibration, thecalculated blood glucose may be expressed as follows:

Blood Glucose Level=(15.0−3)=5.96=71.5 mg/dl

In another example, given a blood glucose reference reading of 95 from atypical blood glucose meter and a Valid ISIG value of 22.1, a resultingSPSR may be determined as 95/22.1=4.3. Since SR<7, the offset=3.Therefore, the MSPSR is 95/[22.1−3]≈5.0. Note that if the SPSR isgreater than or equal to 7 the offset value is 0 and therefore theMSPSR=SPSR.

In alternative embodiments, the offset value may be eliminated from theexpression for calculating the blood glucose value as follows:

Blood Glucose Level=Valid ISIG*MSPSR

The threshold value of 7 and the associated offset of 3 have beenempirically selected based on the characteristics observed from testinga particular type of glucose sensors 12, such as those described in U.S.Pat. No. 5,391,250 entitled “Method of Fabricating Thin Film Sensors”,and U.S. Pat. No. 6,360,888. Other threshold values may be used inconjunction with other offset values to optimize the accuracy of thecalculated MSPSR for various types of glucose sensors 12 and sensorsused to detect other body characteristics. In fact, many thresholdvalues may be used to select between many offset values. An exampleusing two different threshold values (4 and 7) to select between threedifferent offset values (5, 3 and 0) follows:

if SPSR<4, offset=5;

if 4≤SPSR<7, offset=3; and

if SPSR≥7, offset=0.

In particular embodiments an MSPSR may be compared to a validsensitivity range to determine whether a newly calculated MSPSR isreasonable. In order to identify potential system problems, a validMSPSR range of 1.5 to 15 may be employed, for example. However this ismerely an example of such a range and claimed subject matter is notlimited in this respect. This range may be determined based, at least inpart, upon valid glucose sensor sensitivity measurements made in-vitro.MSPSR values outside this range may result in a calibration error alarm(CAL ERROR) to notify the user of a potential problem. Other validsensitivity ranges may be applied depending on the types of sensors tobe calibrated, the range of acceptable sensitivity levels for thevarious sensor types, the manufacturing consistency expected for thesensors, environmental conditions, how long the sensor has been in use,and/or the like.

Particular embodiments may augment the above described single-pointcalibration technique using a modified linear regression technique(shown in a block diagram in FIG. 15 a ) if more than one pairedcalibration data point is available. As shown in FIG. 14 , pairedcalibration data points 800 may linearly regressed by a least squaresmethod to calculate a best fit straight line 802 correlated with pairedcalibration data points 800. The slope of the line resulting from thelinear regression may be the linear regression sensitivity ratio (LRSR)used as the calibration factor to calibrate the glucose monitor 100.

Linear and nonlinear least squares regression may apply an assumptionthat each data point provides equal information about a deterministicpart of a total variation in a value or outcome. In such processes astandard deviation of an error associated with a value would be constantfor all estimated predictions, for example. In some processes this isnot the case. For example, in real-time continuous glucose monitoringusing an enzymatic minimally invasive biosensor to estimate plasmaglucose concentrations as discussed above, an unequal error distributionmay exist. Here, a scatter plot of FIG. 15 b illustrates severalcalibrated glucose sensor points plotted against paired blood glucosereference values throughout a large glycemic range in one particularimplementation. It can be observed from the plot that the accuracy ofthe sensor glucose measurements decreases as the reference blood glucosevalues increase. Such a decreasing accuracy may be measured as varianceand/or standard deviation of an error associated with such measurementsthat increases with blood glucose concentration and/or paired referenceblood glucose reference value. Accordingly, in certain circumstances itmay be advantageous not to treat every observation equally, and apply aweighted least squares regression, for example. This may be implementedaccording to a particular embodiment by giving each point an appropriateweight to control an amount of influence over parameter determination.In doing this, points with less precise influence may be weighted lessin computing a linear regression, while points with more influence maybe more heavily weighted.

In a particular implementation, paired calibration points, comprisingsample values associated with blood-glucose concentration sensormeasurements paired with reference measurements at block 852, may belinearly regressed at block 854 to determine an LRSR. As pointed outabove, in particular embodiments, such a regression may weightparticular pairs and/or sample values according to a degree of certaintyassociated with the accuracy of such sample values based upon a prioriinformation. Such a linear regression calibration may be computed asfollows:

${LRSR} = {\frac{\sum\limits_{i = 1}^{N}{\alpha_{i}.\beta_{i}.{isig}_{i}.{BG}_{i}}}{\sum\limits_{i = 1}^{N}{\alpha_{i}.\beta_{i}.{isig}_{i}^{2}}}.}$

where:

-   -   isig_(i) is a value representing a sensor measurement of a blood        glucose concentration for paired calibration point i;    -   α_(i) is weighting applied to paired calibration point i based        upon the time that the associated sample was obtained;    -   BG_(i) is reference sample of a blood glucose concentration for        paired calibration point i;    -   β_(i) is a weighting applied to paired calibration point i based        upon a degree of certainty associated with accuracy of isig_(i)        as a measurement of blood glucose concentration; and    -   N is a number of paired calibration data points which are to be        linearly regressed.

Accordingly, an estimate of a calibrated blood glucose level may beexpressed as follows:

Blood Glucose Level=Valid ISIG*LRSR

In a particular implementation, a paired calibration point may beweighted according to a time associated with when associated sensormeasurements and reference values are obtained. Here, for example, pairsbased on more recent measurements and reference values may be associatedwith an error with a smaller variance than pairs based on measurementsand reference values obtained in the more distant past. Accordingly, theweight α_(i) applied to calibration pairs may decrease the more distantin the past such calibration pairs are obtained.

Also, as pointed out above, variances associated with measurement errorsin calibrating continuous glucose monitors may not be constant across adynamic range of blood glucose values. Here, in one particularembodiment, weighting β_(i) may represent an inverse variance weighting.In other words, contribution of each data point may be weighted with theinverse of the variance for that set of blood glucose values. Forexample, a set of sensor current values were paired (N=90,000 points)and the inverse variance of sensor current calculated for each bloodglucose reference value as follows:

β_(i)=[var(isig_(i))]⁻¹

Here, application of such an inverse variance to calibration pairs toweight samples for linear regression is merely one example of how suchcalibration pairs may be weighted based upon a decreasing accuracy ofsensor measurements, and claimed subject matter is not limited in thisrespect. Furthermore, it should be understood that a variance orstandard deviation are merely examples of how a statistical dispersionof sensor measurement errors may be quantified, and that other metricsmay be used. In alternative embodiments, for example, β_(i) may bederived as the inverse of an estimate or approximation of the varianceof isig_(i). Also, as discussed below, appropriate weights may bederived from other functions for determining a weight based, at least inpart, on blood glucose reference samples and/or blood glucoseconcentration.

In this particular implementation, however, β_(i) represents an inversevariance and/or standard deviation of all sensor samples (isig_(i))measured at a time corresponding to when reference blood glucose samplevalues i were acquired. In one particular example, inverse varianceweights are plotted in FIG. 15 c for blood glucose values ranging from40-400 mg/dL. Again, it should be understood, however, that the use ofan inverse variance is merely one example of how calibration pairs maybe weighted based upon a degree of certainty associated with accuracy ofsensor measurements and claimed subject matter is not limited in thisrespect.

Alternatively, weights (for application to calibration pairs in a linearregression) may be obtained from a function based on an inverse varianceweights. Here, use of such a function may provide a high qualityestimate that removes noise present in the inverse variance weightsarising from sources such as, for example, variability betweenblood-glucose and a blood glucose monitor. This is illustrated in FIG.15 d where a best line fit is produced by regressing the square root ofthe variance or standard deviation. For the particular example of sensormeasurement samples shown in FIG. 15 b , weights may be determinedaccording to the corresponding function derived from such a best linefit as follows:

$w_{i} = \frac{1}{( {1.787 + {0.0291.i}} )^{2}}$

FIG. 15 e shows a plot of inverse variance β_(i) and function derivedfrom such a best line fit of variance/standard deviation as a functionof ISIG weights w_(i) over a range of blood glucose concentration rangefrom 0 to 400 mg/dl. An inverse variance is plotted as 902 while aweighting function is plotted as 900. As can be observed, the weightingfunction 900 removes noise in the inverse variance to provide aweighting function to be applied to calibration pairs that is adecreasing function of blood glucose concentration and/or associatedblood sample reference values associated with such calibration pairs.

It should be observed that this particular linear regression uses afixed intercept of zero. In other words, if the Valid ISIG is 0 theblood glucose value is 0. Accordingly, this particular linear regressionmethod estimates only one regression parameter, the slope. Inalternative embodiments, other linear regression methods may be usedthat estimate additional regression parameters such as an offset value.

At block 856, particular embodiments may select an offset value for usein calculating a modified linear regression calibration. The purpose ofsuch an offset value, as described above for the single-pointcalibration, is to compensate for an observation that more sensitiveglucose sensors 12 may have a less linear performance at very high bloodglucose levels. If an LRSR for a particular glucose sensor 12, ascalculated in the linear regression calibration expression above, isless than a sensitivity threshold value, then a modified linearregression sensitivity ratio (MLRSR) may be calculated using an offsetvalue included in a modified linear regression calibration expression.In one particular embodiment, for example, such a sensitivity thresholdmay be 7. Here, if an initial calculation of an LRSR is less than 7, anoffset value of 3 may be used to calculate an MLRSR. If an initialcalculation of LRSR yields a value of 7 or greater, an offset value of 0may be used. Thus, MLRSR may be calculated at block 858 using theselected offset value in the modified linear regression calibrationaccording to the following expression:

${MLRSR} = \frac{\sum\limits_{i = 1}^{N}{{\alpha_{i}.\beta_{i}.\lbrack {{isig}_{i}.{- {offset}}} \rbrack}{BG}_{i}}}{\sum\limits_{i = 1}^{N}{\alpha_{i}.\beta_{i}.\lbrack {{isig}_{i} - {offset}} \rbrack^{2}}}$

Accordingly, a calculated blood glucose level may be estimated at block860 as follows:

Blood Glucose Level=(Valid ISIG−offset)*MLRSR

Just as in the case of single-point calibration techniques describedabove, other threshold values may be used at block 856 in conjunctionwith other offset values in the modified linear regression calibrationequation to optimize the accuracy of the calculated MLRSR for varioustypes of glucose sensors 12 and other characteristic sensors.

In particular embodiments, a newly calculated MLRSR may be compared to avalid sensitivity range to determine whether the newly calculated MLRSRis reasonable. To identify potential system problems, a valid MLRSRrange of 2.0 to 10.0 may be employed. MLRSR values outside this rangemay result in a calibration error alarm (CAL ERROR) to notify a user ofa potential problem. As described above for the single-point calibrationtechniques, other valid sensitivity ranges may be applied.

In particular embodiments, glucose monitor data (e.g., pairedcalibration data points as discussed above) may be linearly regressedover a 24 hour period (or window), and new sensitivity ratios may beused for each 24 hour time period. In other embodiments, a time periodmay be reduced to only a few hours or enlarged to cover the entiremonitoring period with the glucose sensor (e.g., several days—or evenweeks with implanted sensors). In further embodiments, such a timewindow may be fixed at a predetermined size, such as 24 hours, 12 hours,6 hours, and/or the like, and the window is moved along over theoperational life of the sensor.

In particular embodiments, paired calibration data points frommeasurements taken before the last calibration may be used to calculatea new sensitivity ratio. For example, to calibrate the glucose monitorevery 6 hours, a paired calibration data point may be established every6 hours. A linear regression technique described above may be executedusing four paired calibration data points, the most recently acquiredpoint and points obtained from six, twelve and eighteen hours before.Alternatively, a number of paired calibration data points used in thecalibration may be as few as one or as large as the total number ofpaired calibration data points collected since the glucose sensor wasinstalled. In alternative embodiments, a number of paired calibrationdata points used in a calibration computation may grow or shrink duringthe life of the glucose sensor due to glucose sensor anomalies.

In still other embodiments, decay characteristics of glucose sensor 12over time may be factored into the equation to account for knowndegradation characteristics of glucose sensor 12 due to sitecharacteristics, enzyme depletion, body movement, and/or the like.Considering these additional parameters in the calibration equation maymore accurately tailor calibration computations used by the glucosemonitor 100 or post processor 200. In particular embodiments, otherparameters may be measured along with the blood glucose such as,temperature, pH, salinity, and/or the like. These other parameters maybe used to calibrate the glucose sensor using non-linear techniques.

In a particular embodiment, real-time calibration adjustment can beperformed to account for changes in the sensor sensitivity during thelifespan of the glucose sensor 12 and to detect when a sensor fails.FIG. 16 (in conjunction with FIGS. 17, 18 a and 18 b) describes thelogic of a self-adjusting calibration technique to adjust thecalibration formula or detect a sensor failure in accordance with oneparticular implementation.

At block 1000, a user may obtain a blood glucose reference from a commonglucose meter, or another blood glucose measuring device, andimmediately enter the blood glucose reference reading into glucosemonitor 100. For every such meter blood glucose entry, an instantaneouscalibration check may be performed and compared to an expected range ofthe value of the calibration check, as in block 1010. In particularembodiments, a Calibration Factor current is calculated (e.g., CFc=MeterBG/current ISIG value) to determine if the CFc (Calibration Factorcurrent) ratio is between 1.5 to 12 (“Criteria 1”), one criterion for anaccurate ISIG value in a particular implementation. If data is outsidethis range, raising a likelihood of a sensor failure or incorrectdetermination/entry of a meter BG value, a Cal Error alarm may betriggered at block 1030 and the Recalibration Variable (Recal), which isoriginally set at NOFAIL may be changed to FAILC1. At this point,another blood glucose reference reading may be requested and enteredinto the glucose monitor 100 to determine whether there was indeed asensor failure or the Meter Blood Glucose value was incorrectlyinputted. The previous Metered Blood Glucose value that generated theerror can be thrown out completely. If Criteria 1 is again not satisfiedat block 1010, an end of the sensor life message may be generated atblock 1040 since then the Recal variable would be recognized as FAILC1at block 1020. However, if Criteria 1 is met at block 1010, then block1200 may determine whether the Recal variable is not equal to FAILC2.Here, the Recal variable is set to FAILC2 only if Criteria 2a is notmet, which is discussed below: Given that the Recal variable at thispoint may only be set to a NOFAIL or FAILC1, logic proceeds to block1210.

Block 1210, a check is performed to determine whether an existingcalibration slope estimation (Previous Estimated Slope or PES) is muchdifferent from the CFc performed using a new meter blood glucose value.A significant difference may indicate a sensor failure, for example. Ina particular embodiment, a difference between a previous estimated slope(PES) and a CFc in terms of percentage (threshold 1) and mg/dl(threshold 2) may be performed. Thresholds 1 and 2 may be set dependingon particular sensor characteristics. In a particular implementation, anexample of checking such changes between the PES and CFc may beperformed as follows:

|1−PES/CFc|*100>threshold 1; and

|CFc−PES|*isig>threshold 2.

If threshold 1 and/or threshold 2 are exceeded according to the aboveexpressions (collectively “Criteria 2a”), then depending on the Recalvariable (at block 1220), either trigger an end of sensor message may betriggered at block 1040 (if the Recal variable is equal to FAILC1 orFAILC2 at block 1220) or a Cal Error alarm may be generated at block1230 (if the Recal variable is equal to NOFAIL at block 1220). Here, ifa Cal Error alarm is generated at block 1230, the Recal variable may beset to FAILC2, the current meter blood glucose reading will be stored asMBGp (Meter Blood Glucose previous), and another blood glucose referenceis requested and entered into the glucose monitor 100 (as MBGc) at block1000. By requesting a new meter blood glucose reading, a comparison canbe made between the last meter blood glucose reading stored at block1230, and the new meter blood glucose reading entered at block 1000 maybe used to determine whether there was a sensor failure. The logicfollows the same paths as described above after block 1000 until thelogic reaches block 1200. At block 1200, since Recal variable is now setto FAILC2 at block 1230, a difference between the previous calibrationcheck (CFp), which generated the FAILC2 alert, and the CFc is performedat block 1300. In particular implementations, the difference between theprevious calibration check and the current calibration check in terms ofpercentage (threshold 1) and mg/dl (threshold 2) may also be performed.In addition, a check is performed to determine whether there has been adirectional change between the CFp and CFc (collectively “criteria 2b”).An example of criteria 2b may be expressed as follows:

|1−CFp/CFc|*100>threshold 1;

|CFc−CFp|*Isig>threshold 2; and

(CFp−PES)*(CFc−CFp)>0.

If the percentage and absolute difference exceeds threshold 1 andthreshold 2, and there is no directional change in the slope with thesecond blood glucose meter reading, then an end of sensor message willbe triggered at block 1040. If criteria 2b is met, then the logicproceeds to block 1310. At block 1310, the logic then determines whetherthe difference between the previous value and the current value was dueto a change in sensitivity of the sensor or whether the reading ismerely noise. In the preferred embodiment, the determination of changein sensitivity versus noise is made by using Criteria 3b. Criteria 3bcompares the difference between (the PES and CFc) and (the CFp versusthe CFc) at block 1420. For example:

|PES−CFc|<|CFp−CFc|

As illustrated in FIG. 17 a , if a difference between PES and CFc isless than a difference between CFp and CFc, criteria 3b will be met,indicating that the previous CFp is an outlier reading (e.g., ananomaly). Then, the MBGp (Meter Blood Glucose previous) is removed atblock 1320 and only the MBGc paired with a valid ISIG is used in theslope calculation, which is resumed at block 1430 and applied ininterpreting the sensor readings at block 1130.

As illustrated in FIG. 17 b , if criteria 3b shows that a differencebetween the PES and CFc is greater than a difference between CFp andCFc, criteria 3b would not be met, indicating a change in sensorsensitivity. A slope calculation may then be fine-tuned by creating anew (artificial) meter blood glucose value (MBGN) with a paired ISIGaccording to the last slope (Seeding) at block 1330. Using the newpaired MBG (MBGN) with the paired MBGp and MBGc, the slope calculationmay be restarted (or reset) at block 1340, as seen in FIG. 17 b . Sensorcalculation may then be performed using a new slope calculation at block1130. By resetting a slope calculation, such a slope calculation canthus be modified automatically to account for changes in sensorsensitivity.

Continuing the logic from block 1210, if the percentage and/or absolutedifference between the PES and CFc is within threshold 1 and/orthreshold 2 at block 1210, indicating a valid calibration, the Recalvariable is again checked at block 1400. If the Recal variable is equalto FAILC1 (indicating that the meter BG was checked twice), anyfine-tuning determination may be skipped and the MBGc may be paired witha valid ISIG for use in updating a slope calculation at block 1430 andapplied in interpreting sensor readings at block 1130. If the RecalVariable is not equal to FAILC1, then the logic may decide whetherfine-tuning the slope calculation is needed at blocks 1410 and 1420. Inparticular embodiments, a decision to fine-tune may be first made bycomparing a percentage and/or absolute difference between the PES andCFc (as done in block 1210) with a threshold 3 and/or a threshold 4(“Criteria 4”) at block 1410 as follows:

|1−PES/CFc|*100<threshold 3; and

|CFc−PES|*isig<threshold 4.

Again, threshold 3 and 4 may be determined based, at least in part, onparticular sensor characteristics. If a percentage and/or absolutedifference between PES and CFc is less than threshold 3 and/or threshold4 at block 1410 (i.e. Criteria 4 met), then the slope calculation cansimply be updated with the new MBGc and paired ISIG value at block 1430,and applied in interpreting the sensor readings at block 1130.

On the other hand, if the Criteria 4 is not met at block 1410, block1420 may determine whether the difference between the expected value andthe current value was due to a change in sensitivity of the sensor orwhether the reading is merely noise. In one particular implementation,such a determination of change in sensitivity versus noise may be madeby using Criteria 3a. Here, criteria 3a CFc and a CFp at block 1420 asfollows:

|PES−CFp|<|CFc−CFp|

As seen in FIG. 18 a , if the difference between a PES and CFp is lessthan a difference between CFc and the CFp, criteria 3a may be met,indicating that an error between predicted and actual values for the CFcwas due to noise in previous calibrations or beginning of a change insensor sensitivity which may be picked up in a subsequent calibrationcycle. Slope calculation may then be updated with a new paired bloodglucose entry (MBGc) at block 1430 and applied in interpreting sensorreadings at block 1130.

As seen in FIG. 18 b , if criteria 3a shows that a difference betweenthe PES and the previous valid calibration check is greater than adifference between the previous valid CFp and the CFc, criteria 3b wouldnot be met, indicating a change in the sensor sensitivity and finetuning is performed. Here, such fine tuning may be performed if two MBGentries in succession indicate a change in slope. Slope calculation maybe fine-tuned by creating a new (artificial) MBGN with a paired ISIGaccording to the last slope (Seeding) at block 1330. Using such a newpaired MBGN with the paired MBGp and MBGc, a slope calculation may berestarted (or reset) at block 1340, as seen in FIG. 18 b . The sensorcalculation may then be performed using the new slope calculation atblock 1130. Again, by resetting the slope calculation, the slopecalculation can thus be modified automatically to account for changes insensor sensitivity.

Although the above description described the primary calibrationtechniques in particular embodiments, many modifications can be made tothe above described calibration techniques without deviating fromclaimed subject matter. For example, in alternative embodiments, acalibration factor may be calculated by first using a single-pointtechnique to calculate an MSPSR for each paired calibration data point,and then averaging them together, either unweighted or weighted bytemporal order of by elapsed time.

As discussed above, particular embodiments described herein utilize aleast squares linear regression computation to calibrate the glucosemonitor 100 and/or analyze sensor data using post-processor 200, forexample. However, alternative embodiments may utilize a multiplecomponent linear regression computation with more variables than justthe paired calibration data points discussed above, to account foradditional calibration effecting parameters, such as environment, anindividual user's characteristics, sensor lifetime, manufacturingcharacteristics (such as lot characteristics), deoxidization, enzymeconcentration fluctuation and/or degradation, power supply variations,and/or the like.

In particular implementations, after a first calibration is performed ona particular glucose sensor 12, subsequent calibrations may employ aweighted average using a sensitivity ratio (SPSR, MSPSR, LRSR, or MLRSR)calculated from data collected since the last calibration, and previoussensitivity ratios calculated for previous calibrations. Here, aninitial sensitivity ratio (SR1) may be calculated immediately afterinitialization/stabilization using a paired calibration data point, andused by glucose monitor 100 or post processor 200 until a secondsensitivity ratio (SR2) is calculated. Here, second sensitivity ratioSR2 may comprise an average of SR1 and the sensitivity ratio ascalculated using the paired calibration data points since the initialcalibration (SRday1) as follows:

${{SR}2} = \frac{{{SR}1} + {{SRday}1}}{2}$

The third sensitivity ratio (SR3) is an average of SR2 and thesensitivity ratio as calculated using the paired calibration data pointssince the second calibration (SRday2). The equation is as follows:

${{SR}3} = \frac{{{SR}2} + {{SRday}2}}{2}$

Sensitivity ratios for successive days may be similarly determined asfollows:

${{SR}_{n} = \frac{{SR}_{({n - 1})} + {SRday}_{({n - 1})}}{2}},$

where:

-   -   SR_(n) is the new sensitivity ratio calculated at the beginning        of time period, n, using data from time period (n-1), to be used        by glucose monitor 100, to convert Valid ISIGs measurement        values to blood glucose readings throughout time period n;    -   SR_((n-1)) is a previous sensitivity ratio calculated at the        beginning of time period n-1, using data from time period n-2;        and    -   SRday_((n-1)) is the sensitivity ratio calculated using paired        calibration data points collected since the last calibration.

Alternatively, previous sensitivity ratios may be ignored and SR may becalculated using only the paired calibration data points since the lastcalibration. In another alternative, all previous SRs may be averagedwith the latest SR calculated using only the paired calibration datapoints since the last calibration. In other implementations, the pairedcalibration data points are used to establish an equation for a curverepresenting SR over time. The curve may then used to extrapolate SR tobe used until the next paired calibration data point is entered.

In embodiments that use a post processor 200 to evaluate a sensitivityratio, such a sensitivity ratio may be calculated using pairedcalibration data points over a period of time since a last calibration,and is not averaged with previous sensitivity ratios. A sensitivityratio determined for a period of time may then be applied to the sameperiod of time over which the paired calibration data points werecollected. This may result in a more accurate than the real-time casedescribed above for the glucose monitor 100 because, in the real-timecase, sensitivity ratios from a previous time period must be used tocalculate the blood glucose level in the present time period. If thesensitivity ratio has changed over time, estimation of blood glucoseusing an old sensitivity ratio may introduce an error.

In particular embodiments, once calibration is complete, Valid ISIGvalues may be converted to blood glucose readings based on a particularversion of the sensitivity ratio, and the resulting blood glucosereadings are compared to an out-of-range limit. If such a resultingcalculated blood glucose level is greater than a maximum out-of-rangelimit of 200 mg/dl (or equivalently 3600 mmol/l), the out-of-range alarmis activated. This is a calibration cancellation event, therefore, ISIGvalues are no longer valid once this alarm is activated. Blood glucosereadings are either not calculated, or at least not considered reliable,until the glucose monitor 100 or post processor 200 is re-calibrated.The user may be notified of the alarm and that re-calibration is needed.

In alternative embodiments, higher or lower maximum out-of-range limitsmay be used depending on the sensor characteristics, the characteristicbeing measured, the user's body characteristics, and the like. Inparticular implementations, a minimum out-of-range limit may be used orboth a maximum and a minimum out-of-range limits may be used. In otherparticular embodiments, such out-of-range limits may not cause bloodglucose readings to become invalid and/or re-calibration is notrequired; however, an alarm could still be provided. In additionalparticular embodiments, an alarm may be activated in response to two ormore ISIG values exceeding an out-of-range limit. ISIG values that areout-of-range may be omitted from display.

In alternative embodiments, calibration may be conducted by injecting afluid containing a known value of glucose into the site around theglucose sensor set 10, followed by sending one or more glucose sensorreadings to glucose monitor 100. The readings may then be processed(filtered, smoothed, clipped, averaged, and/or the like) and used alongwith the known glucose value to calculate the SR for the glucose sensor12. Particular alternative embodiments may use a glucose sensor set ofthe type described in U.S. Pat. No. 5,951,521 entitled “A SubcutaneousImplantable Sensor Set Having the Capability To Remove Or Deliver FluidsTo An Insertion Site”.

In other alternative embodiments, glucose sensor 12 may be supplied witha vessel containing a solution with a known glucose concentration to beused as a reference, and glucose sensor 12 is immersed into thereference glucose solution during calibration. Glucose sensor 12 may beshipped in the reference glucose solution, for example. As describedabove, glucose sensor readings may be used to calculate a sensitivityratio given a known (or independently measured) glucose concentration ofthe solution.

In another alternative embodiment, glucose sensors 12 may be calibratedduring a manufacturing process. Sensors from the same manufacturing lothave similar properties may be calibrated using a sampling of glucosesensors 12 from the population and a solution with a known glucoseconcentration. A sensitivity ratio is provided with the glucose sensor12 and is entered into glucose monitor 100 or post processor 200 by theuser or another individual.

In addition, although the particular process of FIG. 18 includesspecific operations occurring in a particular order, in alternativeembodiments, certain of these operations may be performed in a differentorder, modified, or removed while not deviating from claimed subjectmatter. Moreover, other operations may be added to and/or combined withthe above described process without deviating from claimed subjectmatter. For example, although in the particular embodiment of FIG. 16the variable Recal is never reset to no fail, potentially, an additionaloperation may be added to reset Recal to no fail if no cal error alarmsare triggered after a predetermined number of calibrations.

Unless specifically stated otherwise, as apparent from the followingdiscussion, it is appreciated that throughout this specificationdiscussions utilizing terms such as “processing”, “computing”,“calculating”, “determining”, “estimating”, “selecting”, “weighting”,“identifying”, “obtaining”, “representing”, “receiving”, “transmitting”,“storing”, “analyzing”, “creating”, “contracting”, “associating”,“updating”, or the like refer to the actions or processes that may beperformed by a computing platform, such as a computer or a similarelectronic computing device, that manipulates or transforms datarepresented as physical, electronic or magnetic quantities or otherphysical quantities within the computing platform's processors,memories, registers, or other information storage, transmission,reception or display devices. Accordingly, a computing platform refersto a system or a device that includes the ability to process or storedata in the form of signals. Thus, a computing platform, in thiscontext, may comprise hardware, software, firmware or any combinationsthereof. Further, unless specifically stated otherwise, a process asdescribed herein, with reference to flow diagrams or otherwise, may alsobe executed or controlled, in whole or in part, by a computing platform.

It should be noted that, although aspects of the above system, method,or process have been described in a particular order, the specific orderis merely an example of a process and claimed subject matter is ofcourse not limited to the order described. It should also be noted thatthe systems, methods, and processes described herein, may be capable ofbeing performed by one or more computing platforms. In addition, themethods or processes described herein may be capable of being stored ona storage medium as one or more machine readable instructions, that ifexecuted may enable a computing platform to perform one or more actions.“Storage medium” as referred to herein relates to media capable ofstoring information or instructions which may be operated on, orexecuted by, by one or more machines. For example, a storage medium maycomprise one or more storage devices for storing machine-readableinstructions or information. Such storage devices may comprise any oneof several media types including, for example, magnetic, optical orsemiconductor storage media. For further example, one or more computingplatforms may be adapted to perform one or more of the processed ormethods in accordance with claimed subject matter, such as the methodsor processes described herein. However, these are merely examplesrelating to a storage medium and a computing platform and claimedsubject matter is not limited in these respects.

While there has been illustrated and described what are presentlyconsidered to be example features, it will be understood by thoseskilled in the art that various other modifications may be made, andequivalents may be substituted, without departing from claimed subjectmatter. Additionally, many modifications may be made to adapt aparticular situation to the teachings of claimed subject matter withoutdeparting from the central concept described herein. Therefore, it isintended that claimed subject matter not be limited to the particularexamples disclosed, but that such claimed subject matter may alsoinclude all aspects falling within the scope of appended claims, andequivalents thereof.

1-44. (canceled) 45: A processor-implemented method for calibration, themethod comprising: obtaining a plurality of sample values of anelectrical signal generated by a sensor, each sample value of theplurality of sample values being indicative of a concentration of ananalyte in a first type of bodily fluid of a patient; obtaining aplurality of reference measurement values, each reference measurementvalue of the plurality of reference measurement values corresponding toa concentration of the analyte in a second type of bodily fluid of thepatient; temporally associating a first sample value of the plurality ofsample values to a first reference measurement value of the plurality ofreference measurement values; temporally associating a second samplevalue of the plurality of sample values to a second referencemeasurement value of the plurality of reference measurement values, thesecond reference measurement value being greater than the firstreference measurement value; applying a first weighting to the firstsample value to generate a first weighted sample value; applying asecond weighting to the second sample value to generate a secondweighted sample value, wherein the first weighting is greater than thesecond weighting; determining a calibration factor based, at least inpart, on the first weighted sample value and the second weighted samplevalue; and calibrating a value of the electrical signal to correspond toan estimated concentration of the analyte in the second type of bodilyfluid based on applying the calibration factor to the value of theelectrical signal. 46: The method of claim 45, wherein the first type ofbodily fluid is interstitial fluid. 47: The method of claim 45, whereinthe second type of bodily fluid is blood. 48: The method of claim 45,wherein the analyte is glucose. 49: The method of claim 45, whereindetermining the calibration factor comprises determining a linearregression based on at least the first weighted sample value, the secondweighted sample value, the first reference measurement value, and thesecond reference measurement value. 50: The method of claim 45, whereindetermining the calibration factor comprises: determining a linearregression sensitivity ratio based, at least in part, on the firstweighted sample value, the second weighted sample value, the firstreference measurement value and the second reference measurement value;selecting an offset based, at least in part, on the determined linearregression sensitivity ratio; and determining a modified linearregression sensitivity ratio based, at least in part, on the selectedoffset, the first weighted sample value, the second weighted samplevalue, the first reference measurement value, and the second referencemeasurement value. 51: The method of claim 45, wherein determining thecalibration factor comprises determining a linear relationship based onat least the first weighted sample value and the second weighted samplevalue. 52: A system comprising: one or more processors; and one or moreprocessor-readable media storing instructions which, when executed bythe one or more processors, cause performance of: obtaining a pluralityof sample values of an electrical signal generated by a sensor, eachsample value of the plurality of sample values being indicative of aconcentration of an analyte in a first type of bodily fluid of apatient; obtaining a plurality of reference measurement values, eachreference measurement value of the plurality of reference measurementvalues corresponding to a concentration of the analyte in a second typeof bodily fluid of the patient; temporally associating a first samplevalue of the plurality of sample values to a first reference measurementvalue of the plurality of reference measurement values; temporallyassociating a second sample value of the plurality of sample values to asecond reference measurement value of the plurality of referencemeasurement values, the second reference measurement value being greaterthan the first reference measurement value; applying a first weightingto the first sample value to generate a first weighted sample value;applying a second weighting to the second sample value to generate asecond weighted sample value, wherein the first weighting is greaterthan the second weighting; determining a calibration factor based, atleast in part, on the first weighted sample value and the secondweighted sample value; and calibrating a value of the electrical signalto correspond to an estimated concentration of the analyte in the secondtype of bodily fluid based on applying the calibration factor to thevalue of the electrical signal. 53: The system of claim 52, wherein thefirst type of bodily fluid is interstitial fluid. 54: The system ofclaim 52, wherein the second type of bodily fluid is blood. 55: Thesystem of claim 52, wherein the analyte is glucose. 56: The system ofclaim 52, wherein determining the calibration factor comprisesdetermining a linear regression of at least the first weighted samplevalue, the second weighted sample value, the first reference measurementvalue, and the second reference measurement value. 57: The system ofclaim 52, wherein determining the calibration factor comprises:determining a linear regression sensitivity ratio based, at least inpart, on the first weighted sample value, the second weighted samplevalue, the first reference measurement value and the second referencemeasurement value; selecting an offset based, at least in part, on thedetermined linear regression sensitivity ratio; and determining amodified linear regression sensitivity ratio based, at least in part, onthe selected offset, the first weighted sample value, the secondweighted sample value, the first reference measurement value, and thesecond reference measurement value. 58: The system of claim 52, whereindetermining the calibration factor comprises determining a linearrelationship based on at least the first weighted sample value and thesecond weighted sample value. 59: One or more non-transitoryprocessor-readable media storing instructions, which, when executed byone or more processors, cause performance of: obtaining a plurality ofsample values of an electrical signal generated by a sensor, each samplevalue of the plurality of sample values being indicative of aconcentration of an analyte in a first type of bodily fluid of apatient; obtaining a plurality of reference measurement values, eachreference measurement value of the plurality of reference measurementvalues corresponding to a concentration of the analyte in a second typeof bodily fluid of the patient; temporally associating a first samplevalue of the plurality of sample values to a first reference measurementvalue of the plurality of reference measurement values; temporallyassociating a second sample value of the plurality of sample values to asecond reference measurement value of the plurality of referencemeasurement values, the second reference measurement value being greaterthan the first reference measurement value; applying a first weightingto the first sample value to generate a first weighted sample value;applying a second weighting to the second sample value to generate asecond weighted sample value, wherein the first weighting is greaterthan the second weighting; determining a calibration factor based, atleast in part, on the first weighted sample value and the secondweighted sample value; and calibrating a value of the electrical signalto correspond to an estimated concentration of the analyte in the secondtype of bodily fluid based on applying the calibration factor to thevalue of the electrical signal. 60: The one or more non-transitoryprocessor-readable media of claim 59, wherein the first type of bodilyfluid is interstitial fluid. 61: The one or more non-transitoryprocessor-readable media of claim 59, wherein the second type of bodilyfluid is blood. 62: The one or more non-transitory processor-readablemedia of claim 59, wherein determining the calibration factor comprisesdetermining a linear regression of at least the first weighted samplevalue, the second weighted sample value, the first reference measurementvalue, and the second reference measurement value. 63: The one or morenon-transitory processor-readable media of claim 59, wherein determiningthe calibration factor comprises: determining a linear regressionsensitivity ratio based, at least in part, on the first weighted samplevalue, the second weighted sample value, the first reference measurementvalue and the second reference measurement value; selecting an offsetbased, at least in part, on the determined linear regression sensitivityratio; and determining a modified linear regression sensitivity ratiobased, at least in part, on the selected offset, the first weightedsample value, the second weighted sample value, the first referencemeasurement value, and the second reference measurement value. 64: Theone or more non-transitory processor-readable media of claim 59, whereindetermining the calibration factor comprises determining a linearrelationship based on at least the first weighted sample value and thesecond weighted sample value.